In the wireless communication system, channel estimation is necessary. At the receiving end, the corresponding estimation needs to be carried out on the channel firstly during signal detection and the measurement of various parameters, and then operations such as signal detection is performed by using the estimated channel coefficient. In the 3GPP (3rd Generation Partnership Project) LTE (Long Term Evolution) system, the OFDMA (Orthogonal Frequency Division Multiple Access) technology is employed, therefore, the channels in the time domain and frequency domain need to be estimated.
The channel estimation in the OFDM (Orthogonal Frequency Division Multiplexing) system takes the 2-D Wiener estimator as the best estimator theoretically, and the estimator can be used to carry out channel estimation on all the subcarriers in the OFDM system.
Currently, there are mainly two commonly-used LTE terminal channel estimation methods:
I. the time domain interpolation is firstly carried out and then frequency domain interpolation is carried out; the channel estimation on each OFDM symbol can be dynamically calculated by way of 3 times upsampling frequency domain interpolation, however in this method, the calculation complexity is very high since there are too many frequency domain interpolations.
II. the frequency domain interpolation is firstly carried out and then time domain interpolation is carried out; the 6 times upsampling frequency domain interpolation method can be used for the OFDM symbol containing RS (reference signal) and the channel estimation on the OFDM symbol which doesn't contain RS is calculated by using the interpolation result. The defects of this method lie in: on one hand, the frequency domain interpolation result has to be stored, resulting in large storage amount; and on the other hand, the frequency domain interpolation is performed by using the 6 times upsampling method, the loss of the channel estimation performance is significant, especially under the channel condition that the frequency selectivity is strong.